CN102854291A - Quality determination of peanuts suitable for peanut oil processing, and evaluation method thereof - Google Patents
Quality determination of peanuts suitable for peanut oil processing, and evaluation method thereof Download PDFInfo
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Abstract
The present invention discloses quality determination of peanuts suitable for peanut oil processing, and an evaluation method thereof. The quality determination method comprises the following steps: determining crude fat content, oleic acid content, linoleic acid content and the total content of unsaturated fatty acids in a peanut sample requiring determination, wherein the crude fat content, the oleic acid content, the linoleic acid content and the total content of the unsaturated fatty acids are respectively the mass percentages of the crude fat, the oleic acid, the linoleic acid and the total unsaturated fatty acids in the peanut sample requiring determination; and substituting the determination values into a formula (1) to obtain grease oxidation stability of the peanut sample requiring determination. According to the present invention, a SPSS software is adopted to carry out K-means cluster analysis on a comprehensive value of peanut oil quality, and the peanuts are sequentially ordered according to size, and are divided into three classes of a suitable class, a basically suitable class and an unsuitable class.
Description
Technical field
The peanut quality that the present invention relates to a kind of suitable peanut oil processing is measured and evaluation method.
Background technology
Peanut (Arachis hypogaea L.) belongs to pulse family, originates from South America tropical and subtropical zone area, is a kind of important oilseed protein resource, and oleaginousness reaches 46%～52% in the peanut, and unsaturated fatty acid content reaches more than 85%.The ratio of China's peanut oil expression accounts for 53% of peanut total production, produces about 2,000,000 tons of peanut oil per year, in recent years peanut oil consumption figure sustainable growth.Peanut oil is nutritious, and smell delicate fragrance is pure, is good cooking oil, is one of at present most popular edible oil.The oxidation stability of grease is to weigh the important indicator of peanut oil quality quality, and healthy closely bound up with people, therefore has good oxidation stability peanut oil and be at present mainly focus.Studies show that, therefore different cultivars peanut quality significant difference furthers investigate the different peanut varieties quality characteristic, and filtering out the peanut varieties with high-quality Oil stability is an important development direction of peanut industry.
Peanut quality is numerous, and how each quality affects the peanut oil oxidation stability, is one of present urgent problem.Have the supervision principle component regression to be widely used in modern agriculture science and related discipline, be usually used in the selection of near-infrared spectrum wavelength, the effect of " fewer but better " reaction problem has been played in choosing and the research of sickness influence factor of environmental pollution index really.And the peanut quality characteristic that how to adopt " fewer but better " reflects that the oil quality of peanut protein yet there are no report.
Summary of the invention
The peanut quality that the purpose of this invention is to provide a kind of suitable peanut oil processing is measured and evaluation method, by analyzing the relation between peanut quality and the peanut oil quality, employing has the supervision principle component regression to set up the peanut quality evaluation model of suitable processing peanut oil, for utilization, evaluation and the seed selection of peanut specific breed provides theoretical foundation.
The peanut quality assay method of a kind of suitable peanut oil processing provided by the present invention comprises the steps:
Measure crude fat content, oleic acid content, linoleic acid content and the unsaturated fatty acid content of peanut sample to be measured; Wherein:
Crude fat content, oleic acid content, linoleic acid content and unsaturated fatty acid total content are respectively the quality percentage composition that crude fat, oleic acid, linoleic acid and unsaturated fatty acid total amount account for peanut sample to be measured;
Above-mentioned each measured value substitution to formula (1), is namely obtained the oil oxidative stability of peanut sample to be measured;
Y=-0.412547 * crude fat content+40.560138 * oleic acid content/linoleic acid content-0.618986 * unsaturated fatty acid total content (1).
The present invention also further provides a kind of peanut quality evaluation method of suitable peanut oil processing, comprises the steps:
Measure the oil oxidative stability of peanut sample to be measured according to above-mentioned method, then according to following 1) ~ 3) standard peanut sample to be measured is classified:
1) if the calculated value of this oil oxidative stability 〉=8.0, then peanut sample to be measured is suitable peanut oil processing;
2) if the calculated value of this oil oxidative stability is 8.0～-10.0, then peanut sample to be measured is basic suitable peanut oil processing;
3) if the calculated value of this oil oxidative stability≤-10.0, then peanut sample to be measured is for being not suitable for peanut oil processing.
The present invention has following beneficial effect:
1, reduce analytical procedure: the oil quality of measuring peanut oil need to extract peanut oil, measures every index of quality of peanut oil; Set up the peanut quality rating model of suitable peanut oil processing among the present invention, can determine the size of peanut oil quality by the quality characteristic of several peanuts; The mensuration of the index such as crude fat, fatty acid can adopt near-infrared analyzer to predict in the model, and is convenient and swift; By the near-infrared analysis of shelled peanut is detected, the indices in the forecast model simultaneously, without any damage, and convenient and swift to shelled peanut.
2, with SPSS software the integrated value of peanut oil quality is carried out the K-means cluster analysis, it is arranged according to size order and be divided into suitable, substantially suitable and be not suitable for three classes.Utilize the peanut quality evaluation model of suitable processing peanut oil and the related coefficient of correlation analysis to determine each evaluation index weight, with the classification of each index value size by the K-means cluster analysis, be divided into suitable, substantially suitable and be not suitable for.Take each character weight as estimating score value, the difference correspondence is corresponding characteristic index separately, and with weighted value as top score, be the I level, all the other the like, give each grading index with corresponding score value, last with the final score of each characteristic index score sum as each variety source, and total points is also carried out the K-means cluster analysis be divided into 3 classes, this result is compared with directly adopting the classification results of peanut oil integrated value, finally form peanut processing suitability evaluation standard.
Description of drawings
Fig. 1 is peanut oil quality integrated value original value and the calculated value fitted figure of 11 peanut samples among the embodiment 2.
Embodiment
Employed experimental technique is conventional method if no special instructions among the following embodiment.
Used material, reagent etc. if no special instructions, all can obtain from commercial channels among the following embodiment.
The foundation of the peanut quality rating model of embodiment 1, suitable peanut oil processing
(1) mensuration of peanut quality
Get the peanut sample of results in 2011 as standard items, 45 samples (meeting the normal distribution rule of peanut colony, as shown in table 1);
45 peanut varieties of table 1
Measure organoleptic quality, physics and chemistry and the nutritional quality of each kind and processing quality index totally 35 indexs; Wherein, each index and assay method thereof and standard are as follows:
Peanut physical behavior: fruit shape: when the fruit shape of peanut sample was hockey stick shape, fruit shape must be divided into 1; When the fruit shape of peanut sample was hump shape, fruit shape must be divided into 2; When the fruit shape of peanut sample was beading shape, fruit shape must be divided into 3; When the fruit shape of peanut sample was common shape, fruit shape must be divided into 4; When the fruit shape of peanut sample was wasp waist shape, fruit shape must be divided into 5; When the fruit shape of peanut sample was Pear-Shaped, fruit shape must be divided into 6; When the fruit shape of peanut sample was silk cocoon shape, fruit shape must be divided into 7; When the fruit shape of peanut sample was axe-shape, fruit shape must be divided into 8; Seed shape: with reference to Luan Wenqi (Luan Wenqi, 1986, Luan Wenqi, Feng Haisheng, Wang Jingshan. the difference between the research of peanut varieties major traits---the Characters and type [J]. China seed industry, 1986,23-7.); Scarlet: with reference to ten thousand book ripple (Wan Shubo, 2008; Wan Shubo. peanut quality is learned [M]. Beijing: Scientia Agricultura Sinica technology publishing house, 2008.); All kinds of fruits are heavy: get at random 100 peanuts, weigh, 3 repetitions are averaged; Hundred benevolence are heavy: get at random 100 shelled peanuts, weigh, 3 repetitions are averaged;
Peanut physics and chemistry and nutrition Quality Analysis: moisture: GB/T 5009,3-2003; Fat content: GB/T 5009,6-2003; Protein content: GB/T 5009,5-2003; Ash content: GB/T 5009,4-2003; Crude fiber content: GB/T 5515-2008; Amino acid content: GB/T 5009.124-2003; Sugar content; Above content all refers to account for the quality percentage composition of peanut
Peanut processing attributional analysis: kernel percent: the 100g peanut strips out the weight/100g of shelled peanut * 100%;
Peanut oil attributional analysis: color and luster: with reference to GB/T5525-85; Moisture and volatile matter content: with reference to GB/T5528 – 1995; Peroxide value: GB/T5538-2005; Acid value: GB/T5530-2005; Unsaponifiables: GB/T5535.1-2008; Induction time: GB/T21121-2007; Iodine number: GB/T5532-2008; Saponification number: GB/T5534-2008; Fatty acid: GB/T 17376-2008, GB/T 17377-1998; VE:GB/T 5009.82-2003; Oil yield: extract fat content in Quality of Peanut Oil/peanut; Sterol and squalene: be the method for reporting in 201110424907.7 the patented claim for " a kind of method of measuring simultaneously plant sterol and squalene in the vegetable oil ", application number according to denomination of invention.
To variation range, average, standard deviation, the coefficient of variation of selected 45 peanut varieties master datas, analyze, the result is as shown in table 2;
The quality characteristic of table 2 peanut varieties
| Luffing | Mean value | The coefficient of variation | The coefficient of variation | |
| Fruit shape | 1.00～8.00 | 5.07 | 1.86 | 36.78 |
| Scarlet | 1.00～9.00 | 5.47 | 1.46 | 26.62 |
| Seed shape | 1.00～5.00 | 2.40 | 1.79 | 74.54 |
| The heavy g of all kinds of fruits | 114.80～285.00 | 183.0667 | 43.42 | 23.72 |
| The heavy g of hundred benevolence | 38.60～120.00 | 72.16 | 18.64 | 25.83 |
| Water cut % | 3.71～7.41 | 5.47 | 0.95 | 17.43 |
| Fat content % | 42.11～58.59 | 51.22 | 3.40 | 6.63 |
| Protein content % | 22.15～31.40 | 26.30 | 1.97 | 7.49 |
| Sugar content % | 2.87～12.59 | 7.30 | 2.56 | 35.08 |
| Ash content % | 2.19～3.46 | 2.57 | 0.20 | 7.86 |
| Robust fibre % | 1.50～6.90 | 2.5333 | 0.82 | 32.28 |
| Myristic acid | 0.00～0.03 | 0.01 | 0.01 | 63.21 |
| Palmitic acid | 4.88～8.00 | 6.34 | 0.73 | 11.50 |
| The palm monoenoic acid | 0.15～2.34 | 0.72 | 0.54 | 75.83 |
| Heptadecanoic acide | 0.00～0.07 | 0.04 | 0.01 | 32.64 |
| 17 carbon monoenoic acids | 0.00～0.11 | 0.02 | 0.02 | 122.22 |
| Stearic acid | 0.88～8.72 | 1.88 | 1.19 | 63.38 |
| Oleic acid | 13.57～25.25 | 19.34 | 2.73 | 14.10 |
| Linoleic acid | 12.50～21.14 | 17.52 | 2.23 | 12.74 |
| Leukotrienes | 0.09～3.51 | 1.10 | 0.79 | 72.06 |
| Arachidic acid | 0.30～0.84 | 0.57 | 0.15 | 25.93 |
| The peanut monoenoic acid | 0.22～0.53 | 0.36 | 0.07 | 20.31 |
| Behenic acid | 0.45～1.27 | 0.91 | 0.21 | 22.95 |
| Lignoceric acid | 0.00～0.55 | 0.33 | 0.14 | 43.13 |
| Oil yield/% | 24.04～54.60 | 38.25 | 5.28 | 13.80 |
| Kernel percent/% | 50.31～79.94 | 69.93 | 5.94 | 8.50 |
| O/L | 0.84～1.72 | 1.12 | 0.23 | 20.63 |
| α-VE | 4.62～17.41 | 10.39 | 2.55 | 24.56 |
| γ-VE | 1.27～8.50 | 3.56 | 1.36 | 38.28 |
| δ-VE | 0.11～0.71 | 0.28 | 0.11 | 39.22 |
| The VE total amount | 8.35～23.39 | 14.23 | 3.45 | 24.25 |
| Campesterol | 0.15～14.23 | 6.58 | 3.37 | 51.28 |
| Stigmasterol | 0.52～37.00 | 10.97 | 7.11 | 64.77 |
| Cupreol | 17.10～63.52 | 38.53 | 9.42 | 24.44 |
| Total sterol | 20.46～109.86 | 56.09 | 16.75 | 29.87 |
| Squalene | 2.01～8.25 | 4.60 | 1.46 | 31.73 |
The coefficient of variation is to weigh a statistic of intensity of variation in one group of data, the coefficient of variation＜the 10%(of four indexs such as fat content, protein content, ash content, kernel percent is respectively 6.63%, 7.97%, 7.86%, 8.50%), the coefficient of variation is less, illustrates that their dispersion degree is less; The coefficient of variation of other index is larger, and a lot of quality discrepancies of this explanation different cultivars peanut are larger.
(2) the peanut oil integrated value is analyzed
1) peanut oil index of quality conversion
In the peanut oil, the quality of the larger oil of some indexs is better, and the quality of the less oil of some indexs is better; Therefore, convenient for subsequent calculations, all evaluation indexes of 45 kind peanut oil are all become be the bigger the better, the result is following as shown in table 3;
The peanut oil quality of 45 peanut varieties of table 3
2) data normalization
With the quality of 45 kind peanut oil all become be the bigger the better after, carry out standardization, be about to each data of each index and deduct average divided by standard deviation (Fan Jincheng, Mei Changlin, data analysis [M] 2002, Science Press).
3) standardized data after will processing the full addition such as is carried out and is designated as Y, is the integrated value of peanut quality, and is as shown in table 4;
The integrated value of 45 peanut varieties of table 4
(3) the peanut protein oil quality is analyzed
The peanut oil quality is to estimate the target factor of peanut quality quality, variation range, average, standard deviation, the coefficient of variation to the master data of selected kind oil are analyzed, as shown in table 5, the coefficient of variation＜the 10%(that finds iodine number and saponification number from table 5 is 6.90% and 6.42%), the coefficient of variation is less, illustrates that their dispersion degree is less; The coefficient of variation of other index is larger, and a lot of quality discrepancies of this explanation different cultivars peanut are larger.
Above analysis result shows, the quality characteristic of testing between the selected peanut varieties differs greatly, test kind wide material sources, and kind is selected comprehensively, and kind has certain representativeness.
The attributional analysis of table 5 peanut oil
(4) the oil foundation of preserved egg white matter peanut quality evaluation model
4.1 being arranged, the supervision principal component analysis (PCA) sets up the peanut quality evaluation model that suitable oil is used processing
It is not use all regressor modelings that the supervision principal component analysis (PCA) is arranged, but only utilize those and relevant variable that the independent variable that concerns than strong correlation is arranged, according to the related coefficient of response variable and each independent variable to regressor set screen, the independent variable that related coefficient is surpassed certain threshold value screens, then the part regressor of newly selecting is carried out principle component regression, adopt has the supervision principle component regression that front 34 kinds of choosing in 45 kinds are carried out the foundation of model herein.
Y value in the table 4 (integrated value) and 41 indexs of peanut are carried out significance analysis, found that following 3 indexs and Y are remarkable, as shown in table 6 on 0.05 level.
Table 6 peanut quality and peanut oil quality integrated value return the significant indexes table
| Sequence number | Index | Conspicuousness |
| 1 | Crude fat content | 0.004 |
| 2 | Oleic acid content/linoleic acid content | 0.034 |
| 3 | The unsaturated fatty acid total content | 0.043 |
4.2 the foundation of regression equation
The dependent variable of setting up regression equation need to meet normal distribution, because therefore the oxidation stability itself of peanut and do not meet normal distribution, carry out Box-Cox with Y and change (Verkaik-Kloosterman, 2011), so that dependent variable meets normal distribution:
Obtain λ=1, i.e. Y1=Y-1
Set up the regression equation of Y1 and each independent variable, can find out that from the regression coefficient of regression equation the P value all is significant on 0.05 level, therefore, each index is all brought in the equation, obtains concrete equation as follows,
Table 7 regression coefficient conspicuousness
| Variable | Coefficient | The F value | The P value |
| Coefficient | 5.9999982 | 173.27 | 0.0001 |
| Crude fat content X1 | -1.3946738 | 8.35 | 0.0072 |
| Oleic acid content/linoleic acid content X2 | 0.6473054 | 1.87 | 0.01822 |
| Unsaturated fatty acid total content X3 | -1.9303204 | 16.45 | 0.0003 |
Y1=5.9999982-1.3946738* crude fat standardized data+0.6473054* oleic acid/linoleic acid standardized data-1.9303204* unsaturated fatty acid standardized data
Y1 in the above-mentioned formula is become Y, simultaneously standardized data is converted into raw data and get final product:
Y=-0.412547*X1+2.560138*X2-0.618986*X3, namely
Y=-0.412547 * crude fat content+40.560138 * oleic acid content/linoleic acid content-0.618986 * unsaturated fatty acid total content
The mensuration of embodiment 2, peanut sample peanut oil quality
Remaining 11 peanut varieties among the embodiment 1 are carried out the mensuration of peanut quality.
With 3 index substitution formula (1) such as the crude fat content of these 11 peanut varieties, oleic acid content/linoleic acid content, unsaturated fatty acid contents, calculate the peanut oil quality of 11 kinds, obtain the integrated value (oxidation stability that namely represents peanut) of the peanut oil of 11 peanut samples, the model predication value of this peanut oil integrated value and chemical assay value more as shown in table 8; And the integrated value of the model calculation and mensuration carried out regretional analysis, the related coefficient of the two is that 0.70(is shown in Figure 1).
The model predication value of the peanut oil of 11 peanut samples of table 8 and the comparison of chemical assay value
The foundation of the peanut quality evaluation method of processing of embodiment 3, suitable oil
Adopt the method for K-means cluster analysis, the peanut oil integrated value is classified, tentatively be divided three classes, determine the cluster centre of every class, the peanut oil integrated value is divided into Three Estate, then 45 peanut varieties that record can be classified, as shown in table 9.
The classification of 45 peanut varieties of table 9
Regression coefficient according to each index in the formula (1) is determined each index weights, adopts K-means cluster analysis and actual conditions, and each evaluation index is divided into I level, II level and III level, and each index weights is as I level score, the like.
The weight of each index in table 10 formula (1)
| Sequence number | Index | Coefficient | Weight |
| 1 | Crude fat content | 1.3946738 | 35 |
| 2 | Oleic acid content/linoleic acid content | 0.6473054 | 16 |
| 3 | The unsaturated fatty acid total content | 1.9303204 | 49 |
3 index of quality of peanut are carried out respectively the K-means cluster analysis, each index is divided into 3 classes, be I level (suiting), II level (substantially suitable) and III level (being not suitable for), and be the I level with each index weights as its top score, all the other the like, give each grading index corresponding score value, as shown in table 11.
The score value of each each grade of index of table 11
Final score so that each characteristic index score sum is cultivated peanut as each product is divided into 3 classes according to K-means cluster analysis formula with each kind final score, and namely I level (suiting), II level (substantially suitable) and III level (being not suitable for) are as shown in table 12.
The classification of table 12 45 peanut varieties that clustering method obtains according to K-means
The result of table 12 result and table 9 is compared, and the two matching degree is: adapted varieties is 80%, and basic adapted varieties is 63.6%, and being not suitable for kind is 85.7%, illustrates that this evaluation result is better, is suitable as suitable processing peanut oil peanut quality evaluation criterion.
The K-means cluster analysis is a kind of algorithm without the supervision formula, and what wherein K represented is final classification number.It is according to classification number K random choose K initial cluster centre, constantly iteration.In iteration each time, calculate and the distance of each cluster centre by each point, and will nearest class as the affiliated class of this point, namely when objective function reached minimum value, obtaining cluster was final cluster result, and data are divided into the K class.K-means algorithm purpose is equivalence class to be carried out in a set divide, namely to the identical record of data structure according to certain classifying rules, it is divided into several record sets of the same type (Xue Jingtao etc., 2010).Objective function adopts the square error criterion, namely
E=∑ ∑ | P-m
i|
2(formula 5.1)
Wherein E is the square error sum of each clustering object, and P is clustering object, m
iClass C
iThe mean value of each clustering object, that is:
In the formula, | c
i| expression be class C
iThe number of clustering object, the computation complexity of K-means clustering procedure are O (knt), and wherein k represents cluster numbers, and n represents the clustering object number of samples, and t represents iterations.
Claims (2)
1. the peanut quality assay method of a suitable peanut oil processing comprises the steps:
Measure crude fat content, oleic acid content, linoleic acid content and the unsaturated fatty acid total content of peanut sample to be measured; Wherein:
Crude fat content, oleic acid content, linoleic acid content and unsaturated fatty acid total content are respectively the quality percentage composition that crude fat, oleic acid, linoleic acid and unsaturated fatty acid total amount account for peanut sample to be measured;
Above-mentioned each measured value substitution to formula (1), is namely obtained the oil oxidative stability of peanut sample to be measured;
Y=-0.412547 * crude fat content+40.560138 * oleic acid content/linoleic acid content-0.618986 * unsaturated fatty acid total content (1).
2. the peanut quality evaluation method of a suitable peanut oil processing comprises the steps:
Method according to claim 1 is measured the oil oxidative stability of peanut sample to be measured, then according to following 1) ~ 3) standard peanut sample to be measured is classified:
1) if the calculated value of this oil oxidative stability 〉=8.0, then peanut sample to be measured is suitable peanut oil processing;
2) if the calculated value of this oil oxidative stability is 8.0～-10.0, then peanut sample to be measured is basic suitable peanut oil processing;
3) if the calculated value of this oil oxidative stability≤-10.0, then peanut sample to be measured is for being not suitable for peanut oil processing.
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| CN107228809A (en) * | 2017-05-09 | 2017-10-03 | 中国农业科学院农产品加工研究所 | The peanut quality evaluation method and device of a kind of suitably leisure peanut processing |
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| CN107228924B (en) * | 2017-06-20 | 2019-08-20 | 中国农业科学院农产品加工研究所 | A kind of adequate proteins processing peanut raw material quality determination and its evaluation method |
| CN108998207A (en) * | 2018-08-07 | 2018-12-14 | 河南正花食品集团有限公司 | A kind of oil expression method preventing peanut oxidative rancidity |
| CN110361509A (en) * | 2019-07-18 | 2019-10-22 | 中国科学院植物研究所 | The method for obtaining the oil quality evaluation model of Paeonia suffruticosa seed |
| CN111855932A (en) * | 2020-07-30 | 2020-10-30 | 青岛农业大学 | Method for identifying oil yield of peanuts and assisting in identifying peanut varieties |
| CN111855932B (en) * | 2020-07-30 | 2022-05-20 | 青岛农业大学 | Method for identifying peanut oil yield and assisting in identifying peanut variety |
| CN112611830A (en) * | 2020-11-30 | 2021-04-06 | 湖北文理学院 | Method for distinguishing varieties of walnuts according to oxidation characteristics of walnuts |
| CN112611830B (en) * | 2020-11-30 | 2022-07-08 | 湖北文理学院 | Method for distinguishing varieties of walnuts according to oxidation characteristics of walnuts |
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